Generic approach to access barriers in dehydrogenation reactions

Yu, Liang; Vilella, Laia; Abild‐Pedersen, Frank

doi:10.1038/s42004-017-0001-z

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…The latter is in close agreement with an experimentally determined barrier of 1.21 eV. 90 The herein computed DFT barrier for the 9-9-9 site is consistent with barriers reported in the theoretical literature for the Pt(111), Ea=2.53-2.73 eV, [79][80][81][82] with the slightly lower barrier found here resulting from the use of a different TS-identification method (fixed bond-length 91 NEB compared to climbing-image NEB, see Methods). While Figure 5 suggest that our proposed model performs well with regards to trends in energetics and rates within one type of site, the model is inconsistent with DFT when comparing rates between different types of sites.…”

Section: Critical Evaluation Of the Modelsupporting

confidence: 90%

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

Stenlid

Streibel

Choksi

et al. 2022

Preprint

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Bimetallic alloys have emerged as an important class of catalytic materials, spanning a wide range of shapes, sizes, and compositions. The combinatorics across this wide materials space makes predicting catalytic turnovers of individual active sites challenging. Herein, we introduce the stability of active sites as a descriptor for site-resolved reaction rates. The site stability unifies structural and compositional variations in a single descriptor. We compute this descriptor using coordination-based models trained with DFT calculations. Our approach enables instantaneous predictions of catalytic turnovers for nanostructures up to 12 nm in size. Using NO decomposition as probe reaction, we identify sites on Au-Pt nanoparticles that, because of local structure and composition, yield one-to-two orders of magnitude increase in rate compared to sites on monometallic Pt. By prescribing specific sizes, morphologies, and compositions of optimal catalytic nanoparticles, our method guides experiments towards designing bimetallic catalysts with optimal turnovers.

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Section: Critical Evaluation Of the Modelsupporting

confidence: 90%

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

Stenlid

Streibel

Choksi

et al. 2022

Preprint

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show abstract

“…The costliest part of the first principles calculations is associated with identifying transition states, needed for the calculation of the respective activation energies. Therefore significant effort has focused on increasing the efficiency of BEP relationships and generalizing them [20,33–35] . Recent promising approaches have combined BEP relationships with machine learning approaches and significantly increased the accuracy of predicted activation energies for a large dataset of various reactions [34,35] .…”

Section: Methodsmentioning

confidence: 99%

“…Therefore significant effort has focused on increasing the efficiency of BEP relationships and generalizing them. [20,[33][34][35] Recent promising approaches have combined BEP relationships with machine learning approaches and significantly increased the accuracy of predicted activation energies for a large dataset of various reactions. [34,35] However, the chosen descriptors either had no physical meaning, [35] or the focus was only on a limited number of reactions, [34,36] both significantly limiting general applicability.…”

mentioning

confidence: 99%

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Göeltl

Mavrikakis

2022

ChemCatChem

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Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

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“…The combination of explicit DFT calculations and simple modeling methods such as scaling relationships has proven to be an essential tool in the computational search for new and promising catalysts. − The scaling relationships can be viewed as the correlation between a descriptor and the transition-state (TS) energy of a specific reaction, − with descriptors typically being the final-state (FS) or adsorption energies of one or a few key intermediates. ,, For the C–H bond activation and (de)hydrogenation reactions of alkanes, several scaling relationships have been proposed. ,,,,− One of the first attempts to generalize a set of de(hydrogenation) reactions for several reactants including methane, ethane, and propane over close-packed and stepped surfaces of TMs was proposed by Wang et al Therein, with a suitable choice of reference systems, the TS scaling relationship was approximated to just one single linear scaling relationship (LSR). LSRs between the FS energies and the corresponding TS energies have been used to understand the effect of coadsorbed species on metal surfaces.…”

Section: Introductionmentioning

confidence: 99%

Trends in the Activation of Light Alkanes on Transition-Metal Surfaces

et al. 2020

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The first (oxidative) dehydrogenation step of light alkanes (ethane, propane, and n-butane) on transition-metal (closed-packed and stepped) surfaces is analyzed using density functional theory (DFT) calculations. It is shown that the transition-state energies (ΔE TS) of the C–H bond activation scale linearly with the corresponding final-state energies (ΔE FS), and all alkanes studied here share the same linear scaling relationships for the nonoxidative, oxygen-assisted, and hydroxyl-assisted reactions. Variations in ΔE TS between alkanes can be mainly attributed to differences in dispersion contributions determined by the carbon-chain length. As the carbon chain increases, the ΔE TS of the alkane C–H bond activation decreases. In addition, the ΔE TS of the first (O)DH step of propane and n-butane is linearly correlated with the ΔE TS of the first ethane (O)DH step. We also find that the oxygen and hydroxyl adsorption energies on the transition-metal surfaces (closed-packed and stepped) are dictating the promoting/poisoning effect of the C–H bond activation. Based on our extensive DFT calculations, we find that Pt has the lowest C–H bond transition-state energy for both the nonoxidative and oxidative pathways, and metals such as Au and Ag become active for C–H bond activation of alkanes only when oxygen and hydroxyl species are present on the metal surfaces. Finally, by establishing scaling relationships over a wide range of transition-metal surfaces, we have developed a simple and highly accurate model for the prediction of C–H bond activation barriers for the (oxidative) dehydrogenation of light alkanes.

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Generic approach to access barriers in dehydrogenation reactions

Cited by 13 publications

References 37 publications

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Trends in the Activation of Light Alkanes on Transition-Metal Surfaces

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